Method and kit for diagnosing autism using gene expression profiling

ABSTRACT

This invention relates to DNA microarray technology, and more specifically to methods and kits for identifying autism and autism spectrum disorders in humans.

PRIORITY

This application claims the benefit under 119(e) of U.S. 60/789,593filed 6 Apr. 2007.

BACKGROUND

1. Field of the Invention

This invention relates to DNA microarray technology, and morespecifically to methods and kits for identifying autism and autismspectrum disorders in humans.

2. Description of the Prior Art

Several publications are referenced in this application in parenthesesin order to more fully describe the state of the art to which thisinvention pertains. Full citations for these references are found at theend of the specification. The disclosure of each of these publicationsis incorporated by reference herein in their entirety.

The autism spectrum encompasses a set of complex multigenicdevelopmental disorders that severely impact the development oflanguage, non-verbal communication, and social skills, and areassociated with odd, stereotyped, repetitive behavior and restrictedinterests. To date, diagnosis of these neurologically based disordersrelies predominantly upon behavioral observations often prompted bydelayed speech or aberrant behavior, and there are no known genes thatcan serve as definitive biomarkers for the disorders.

Autism and related autism spectrum disorders (including Asperger'sSyndrome and pervasive developmental disorder-not otherwise specified(PDD-NOS)) are considered to be among the most devastating psychiatricillnesses affecting children. The three core symptoms of autism spectrumdisorders (ASD) are: 1) deficits in social interactions andunderstanding, 2) aberrant communication and/or language development,and 3) restricted, repetitive, and stereotyped behaviors [1]. To date,there are no definitive molecular or genetic markers that allowunequivocal diagnosis of ASD, with the exceptions of tuberous sclerosis,Rett's Syndrome, and Fragile X Syndrome [2-12]. Together, thesegenetically defined mutations are present in only a minority ofindividuals (<10%) within the broad autism spectrum. The majority ofdiagnoses are dependent on behavioral characteristics, according toDSM-IV guidelines, using questionnaires such as the Autism DiagnosticInterview-Revised (ADI-R) [13] or the Autism Diagnostic ObservationSchedule (ADOS) [14], which are structured to evaluate children who areapproximately 2 or older in mental age. Although the guidelines arerelatively clear, the individual rater's (eg., parents, teachers,clinicians, therapists) perception of the evaluated behavior leaves muchroom for ambiguity. Moreover, with the more mildly affected individuals(eg., with Asperger's Syndrome), diagnosis is often not made until wellafter the child starts school and, even then, the child is oftendiagnosed with other more common disorders (such as attention deficitdisorder or learning disability) before Asperger's Syndrome isconsidered, which delays appropriate intervention and effectiveeducational programming. Thus, there is a great need to identifybiomarkers that can be used consistently in a clinical setting todiagnose ASD. Furthermore, it is important to identify biologicalprocesses that are associated with specific ASD phenotypes in order todesign effective drug therapies targeted to specific individuals.

Although genetic linkage analyses have identified numerous candidategenes for autism [15], there is little consistent data that wouldsupport the use of any (or a combination) of these as biomarkers forASD. Furthermore, each candidate gene alone lends little insight intothe pathophysiology of these disorders, which are believed to arise fromdysregulation of multiple genes. Recently, attention has turned totranscriptional profiling approaches [16-19], which involvesimultaneous, large-scale expression analysis of thousands of genes on acDNA (or oligonucleotide) microarray slide, to unravel complexpsychiatric disorders. The advantage of transcriptional profiling usingmicroarrays is the ability to study multiple genes in the context offunctional gene networks within a living cell, as opposed to forwardgenetic approaches. So far, application of microarrays to the study ofautism has been described in just one study on post-mortem brain tissuefrom autistic subjects and matched tissue controls [20]. Thirty geneswere identified as being differentially expressed in autistic brainsamples relative to matched tissue controls on a combination of 2separate array platforms containing 588 or 9374 cDNA probes, indicatingthat autism is associated with multiple disturbances in gene expression.Of this list, only a few genes related specifically to neurologicalfunctions and, of these, the glutamate receptor system was targeted forfurther study. In a similar vein, a recent bioinformatics analysis ofautism positional candidate genes using biological databases andcomputational gene network prediction software demonstrates that theoften disparate results from genetic studies implicating a multitude ofdifferent genes can be coalesced into interconnected but distinctpathways centered on a specific gene or genes (e.g., FOS and TP53), oron a particular biological theme, e.g., apoptosis [15]. Both of thesestudies suggest the involvement of multiple genes not previouslyassociated with autism and illustrate the power of using a globalapproach to study this complex disorder.

The experimental strategy used in the study reported here was designedto tease out differences in gene expression among genetically identicalindividuals with ASD which might relate to observed differences in thedegree of expression of autistic symptoms. Inasmuch as naturalvariations in gene expression are especially low for monozygotic twins[21, 22], such a strategy has been shown to be useful in identifyingcandidate genes for bipolar disorder [23] and osteoporosis [24].Moreover, lymphoblastoid cell lines (LCL) derived from blood cells ofautistic individuals were used in this study to explore the possibilitythat biomarkers for autism could be expressed in easily accessibleperipheral cells. Indeed, it has been reported previously that LCL fromindividuals with bipolar disorder displayed altered gene expression inboth postmortem brain tissue and lymphoblasts, although one of thegenes, LIM, was altered in the opposite direction in LCL [25]. Follow-upgenetic association analyses of this gene demonstrated association of asingle nucleotide polymorphism with bipolar disorder [26], indicatingthe usefulness of LCL and DNA microarray analyses in identifyingpotential biomarkers of a complex neurological disease.

While studies of gene expression in brain tissue may lead to a betterunderstanding of the mechanistic basis for ASD, it is not an appropriatetarget for diagnostic assays. Ideally, diagnostic assays should useeasily obtained patient samples such as blood, although there is noevidence that gene expression or other markers exist in the peripheralblood of ASD patients. However, one may hypothesize that ASD mightarise, in part, through dysregulation of expression of specific neuronalgenes and that expression differences between affected and unaffectedindividuals might be present in tissues other than brain. As a test ofthis hypothesis, we chose to use DNA microarray analysis to examine geneexpression in LCL derived from peripheral blood lymphocytes.

SUMMARY

Here we report the first study using a genome-scale approach to identifybiomarkers for autism. We demonstrate by gene expression profiling onDNA microarrays that: 1) LCL derived from five monozygotic twin pairsdiscordant for diagnosed autism and/or language impairment showdifferential gene expression; 2) a number of the most differentiallyexpressed genes are present in pathways critical to the development andfunction of the nervous system; 3) there appears to be a quantitativerelationship between the severity of the autistic phenotype exhibited bythe twins and the expression level of certain genes relative to that ofthe respective genes in cell lines from non-affected siblings; and 4)approximately half of the most highly differentially expressed genes mapin silico to previously reported chromosomal regions containing autismsusceptibility genes or quantitative trait loci.

Specifically, one embodiment of the present inventive subject matterincludes a method of screening for an Autism Spectrum Disorder in apatient by analyzing differential gene expression patterns by DNAmicroarray analysis, comprising the steps of: obtaining a nucleic acidsample from peripheral cells of a patient; performing DNA microarrayanalysis on said nucleic acid samples to obtain a gene expressionanalysis data set; and comparing said data set to a control data setcorresponding to a gene ensemble of differentially expressed genesindicative of Autism Spectrum Disorder, wherein Autism Spectrum Disorderis indicated upon observing statistically significant differential geneexpression in 20 or more genes.

In preferred embodiments, the gene ensemble is a gene ensemble accordingto Table 1, a gene ensemble according to Table 3, a gene ensembleaccording to Table 4, or a gene ensemble according to Table 5.

Preferred embodiments also include gene ensembles comprising acombination of genes listed in Tables herein, but specifically Tables 1,3, 4, and 5.

In a preferred embodiment, the peripheral cells comprise lymphoblastoid(lymphoblasts) cells, and more preferably white blood cells. In anotherpreferred embodiment, the peripheral cells are mucosal epithelial cellsfrom buccal swabs, particularly advantageous when considering a neonatalor pediatric patient population.

The control expression profile is preferably of nonautistic individuals,but may also be reflective of autistic individuals depending on how themethod is performed.

The DNA microarray analysis preferably includes screening methodsselected from the group consisting of large scale microarray analysis,qPCR analysis, Western analysis, and focused gene chip analysis.However, other equivalent techniques known to persons of skill in theart are also contemplated as included herein.

The gene ensemble of differentially expressed genes indicative of AutismSpectrum Disorder preferably include genes involved in inflammation, andmore preferably neuroinflammation.

Autism spectrum disorders herein include autism, Asperger's Syndrome,and pervasive developmental disorder-not otherwise specified (PDD-NOS).

The present inventive subject matter also includes an assay forscreening drugs and other agents for ability to treat autism spectrumdisorder or a disease or disorder related thereto, said assay comprisingthe steps of: culturing an observable cellular test colony whichproduces a differential gene expression profile representative of anautism spectrum disorder and which has been inoculated with the drug oragent to be assayed; harvesting a cellular extract from the cellulartest colony; determining the level of gene expression in the testcolony; and comparing the level of gene expression in the test colony toa control level of gene expression which represents a test colony notinoculated with the drug, or to the test colony prior to inoculationwith the drug or agent, wherein the ability of the drug or agent tomodulate the production, stability, degradation or activity of geneexpression is indicative of the drug or agent's ability to treat autismspectrum disorder.

In a preferred embodiment the Autism Spectrum Disorder is indicated uponobserving statistically significant differential gene expression in 20or more genes. In preferred assay embodiments, the differential geneexpression profile representative of an autism spectrum disorder ischaracterized by a gene ensemble selected from gene ensembles accordingto. Table 1, Table 3, Table 4, or Table 5. Preferred embodiments alsoinclude gene ensembles comprising a combination of genes listed inTables herein, but specifically Tables 1, 3, 4, and 5.

Also contemplated herein are test kits to facilitate diagnosis andtreatment of autism spectrum disorders, comprising: an indicator ofexpressed genes representative of a patient that has a differential geneexpression pattern indicative of Autism Spectrum Disorder; reagents andequipment for analyzing a sample of epithelial cells and obtaining adifferential gene expression pattern; and directions for use of saidkit. Such kits are generally well known in the art, with the novelfeatures herein stemming from the differential gene expressionindicative of ASD. In preferred embodiments, the sample comprises whiteblood cells and also mucosal epithelial cells from a buccal swab, i.e. acheek swab. It is contemplated that the kit reagents and equipmentcomprise cDNA microarray analysis materials.

Further preferred embodiments herein include a computer-implementedmethod for analyzing gene expression to screen for an autism spectrumdisorder is provided wherein the method comprises the steps of:compiling data comprising a plurality of measured gene expressionsignals derived from cDNA microarray analysis of lymphoblastoid cellsinto a form suitable for computer-based analysis; and analyzing thecompiled data, wherein the analyzing comprises identifying gene networksfrom a number of uprelated biomarker genes and downregulated biomarkergenes, wherein the biomarker genes are genes that have a reported rolein inflammation. In a preferred embodiment, the genes are ASS, ALOX5AP(FLAP), DAPK1, AND IL6ST, as listed in bold in Table 1. It is alsocontemplated that the biomarker genes are genes involved in nervoussystem development and function. These include ALOX5AP (FLAP), CD44,CHL1, EGR2, F13A1, FLT1, IL6ST, ITGB7, and NAGLU.

Along these lines, the inventive contribution also includes acomputer-readable medium on which is encoded programming code foranalyzing autism spectrum disorder gene expression from a plurality ofdata points which comprises a gene ensemble which is filtered using alog 2 cutoff of 0.58.

TABLE 1 is a chart showing significant up- and down-regulated genes fromSAM analysis of microarray experiments on 3 sets of monozygotic twinsdiscordant for autism diagnosis.

TABLE 2 is a chart showing expression of ASS, CHL1, AND FLAP.

TABLE 3 is a chart showing network focus genes from Ingenuity PathwaysAnalysis.

TABLE 4 shows the relative expression of candidate genes in monozygoticconcordant twin pairs with differential language impairment and in normtwins.

TABLE 5 shows significant genes across give sets of twins with ASD.

TABLE 6 is a global functional analysis.

TABLE 7 shows differentially expressed candidate genes from microarrayexperiments mapped into a silico to autism susceptibility genes.

SUPPLEMENTARY TABLE 2 is a table showing case description of subjectsfrom whom LCL were derived and used in the study.

SUPPLEMENTAL TABLE 3 shows primers used for qualitative RT-PCR analyses.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 Gene networks showing inter-relationship between differentiallyexpressed genes in LCL from 3 discordant autistic twin sets usingIngenuity Pathways Analysis software. The over-expressed (red) andunder-expressed (green) genes were identified as significant using SAManalysis (FRD=26.4%) of microarray data across 3 twin pairs. The log 2expression ratio cutoff was set at ±0.58 and was based upon the meanvalues for each gene. Genes within this network that have a reportedrole in nervous system development and function include: ASS, ALOX5AP(FLAP), DAPK1, F13A1, IL6ST, NAGLU, PTGS2, and ROBO1. Gray genes arepresent but do not meet expression cutoff.

FIG. 2 Gene networks showing inter-relationship between differentiallyexpressed genes in lymphoblastoid cells lines from monozygotic twinsdiscordant in severity of autism spectrum disorder and/or languageimpairment. The over-expressed (red) and under-expressed (green) geneswere identified as significant using SAM analysis (FDR=15.6%) ofmicroarray data across 5 twin pairs. The log 2 expression ratio cutoffwas set at ±0.58 and was based upon the mean values for each gene.Differentially expressed genes within this network that have a reportedrole in nervous system development and function include: ALOX5AP (FLAP),CD44, CHL1, EGR2, F13A1, FLT1, IL6ST, ITGB7, and NAGLU. Gray genes arepresent but do not meet expression cutoff.

FIG. 3 shows a principal components analysis of microarray data from 5sets of monozygotic twins with ASD.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS List of Abbreviations

ADIR: Autism Diagnostic Interview-Revised

ADOS: Autism Diagnostic Observation Schedule AGRE: Autism GeneticResource Exchange

ASD: autism spectrum disorders

ASS: argininosuccinate synthetase

CHL1: cell adhesion molecule with homology to L1CAM

DAPK1: death-associated protein kinase 1

EGR2: early growth response 2 protein (Krox-20)

FLAP: 5-lipoxygenase activating protein

5-HTT: 5-hydroxytryptamine (serotonin) transporter

ITGB7: integrin beta-7LCL: lymphoblastoid cell lines

PPVT: Peabody Picture Vocabulary Test

ROBO1: roundabout, axon guidance receptor

The following definitions are provided to facilitate an understanding ofthe present invention:

With reference to nucleic acids used in the invention, the term“isolated nucleic acid” is sometimes employed. This term, when appliedto DNA, refers to a DNA molecule that is separated from sequences withwhich it is immediately contiguous (in the 5′ and 3′ directions) in thenaturally occurring genome of the organism from which it was derived. An“isolated nucleic acid molecule” may also comprise a cDNA molecule or arecombinant nucleic acid molecule.

When applied to RNA, the term “isolated nucleic acid” refers primarilyto an RNA molecule encoded by an isolated DNA molecule as defined above.Alternatively, the term may refer to an RNA molecule that has beensufficiently separated from other nucleic acids with which it would beassociated in its natural state (i.e., in cells or tissues). An isolatednucleic acid (either DNA or RNA) may further represent a moleculeproduced directly by biological or synthetic means and separated fromother components present during its production.

The term “oligonucleotide,” as used herein refers to sequences andprobes of the present invention, and is defined as a nucleic acidmolecule comprised of two or more ribo- or deoxyribonucleotides,preferably more than three. The exact size of the oligonucleotide willdepend on various factors and on the particular application and use ofthe oligonucleotide.

With respect to single stranded nucleic acids, particularlyoligonucleotides, the term “specifically hybridizing” refers to theassociation between two single-stranded nucleotide molecules ofsufficiently complementary sequence to permit such hybridization underpre-determined conditions generally used in the art (sometimes termed“substantially complementary”). In particular, the term refers tohybridization of an oligonucleotide with a substantially complementarysequence contained within a single-stranded DNA molecule of theinvention, to the substantial exclusion of hybridization of theoligonucleotide with single-stranded nucleic acids of non-complementarysequence. Appropriate conditions enabling specific hybridization ofsingle stranded nucleic acid molecules of varying complementarity arewell known in the art. For instance, one common formula for calculatingthe stringency conditions required to achieve hybridization betweennucleic acid molecules of a specified sequence homology is set forthbelow (Sambrook et al., 1989):

As an illustration of the above formula, using [Na+]=[0.368] and 50%formamide, with GC content of 42% and an average probe size of 200bases, the T_(m) is 57° C. The T_(m) of a DNA duplex decreases by 1-1.5°C. with every 1% decrease in homology. Thus, targets with greater thanabout 75% sequence identity would be observed using a hybridizationtemperature of 42° C.

The term “probe” as used herein refers to an oligonucleotide,polynucleotide or DNA molecule, whether occurring naturally as in apurified restriction enzyme digest or produced synthetically, which iscapable of annealing with or specifically hybridizing to a nucleic acidwith sequences complementary to the probe. The probes of the presentinvention refer specifically to the oligonucleotides attached to a solidsupport in the DNA microarray apparatus such as the glass slide. A probemay be either single-stranded or double-stranded. The exact length ofthe probe will depend upon many factors, including temperature, sourceof probe and use of the method. For example, for diagnosticapplications, depending on the complexity of the target sequence, theoligonucleotide probe typically contains 15-25 or more nucleotides,although it may contain fewer nucleotides. The probes herein areselected to be complementary to different strands of a particular targetnucleic acid sequence. This means that the probes must be sufficientlycomplementary so as to be able to “specifically hybridize” or annealwith their respective target strands under a set of pre-determinedconditions. Therefore, the probe sequence need not reflect the exactcomplementary sequence of the target. For example, a non-complementarynucleotide fragment may be attached to the 5′ or 3′ end of the probe,with the remainder of the probe sequence being complementary to thetarget strand. Alternatively, non-complementary bases or longersequences can be interspersed into the probe, provided that the probesequence has sufficient complementarity with the sequence of the targetnucleic acid to anneal therewith specifically.

The term “primer” as used herein refers to a DNA oligonucleotide, eithersingle-stranded or double-stranded, either derived from a biologicalsystem, generated by restriction enzyme digestion, or producedsynthetically which, when placed in the proper environment, is able tofunctionally act as an initiator of template-dependent nucleic acidsynthesis. When presented with an appropriate nucleic acid template,suitable nucleoside triphosphate precursors of nucleic acids, apolymerase enzyme, suitable cofactors and conditions such as a suitabletemperature and pH, the primer may be extended at its 3′ terminus by theaddition of nucleotides by the action of a polymerase or similaractivity to yield a primer extension product. The primer may vary inlength depending on the particular conditions and requirement of theapplication. For example, in diagnostic applications, theoligonucleotide primer is typically 15-25 or more nucleotides in length.The primer must be of sufficient complementarity to the desired templateto prime the synthesis of the desired extension product, that is, to beable anneal with the desired template strand in a manner sufficient toprovide the 3′ hydroxyl moiety of the primer in appropriatejuxtaposition for use in the initiation of synthesis by a polymerase orsimilar enzyme. It is not required that the primer sequence represent anexact complement of the desired template. For example, anon-complementary nucleotide sequence may be attached to the 5′ end ofan otherwise complementary primer. Alternatively, non-complementarybases may be interspersed within the oligonucleotide primer sequence,provided that the primer sequence has sufficient complementarity withthe sequence of the desired template strand to functionally provide atemplate-primer complex for the synthesis of the extension product.

The term “specific binding pair” as used herein includesantigen-antibody, receptor-hormone, receptor-ligand, agonist-antagonist,lectin-carbohydrate, nucleic acid (RNA or DNA) hybridizing sequences, Fcreceptor or mouse IgG-protein A, avidin-biotin, streptavidin-biotin,amine-reactive agent-amine conjugated molecule and thiol-goldinteractions. Various other determinant-specific binding substancecombinations are contemplated for use in practicing the methods of thisinvention, such as will be apparent to those skilled in the art.

The term “detectably label” is used herein to refer to any substancewhose detection or measurement, either directly or indirectly, byphysical or chemical means, is indicative of the presence of the targetbioentity in the test sample. Representative examples of usefuldetectable labels, include, but are not limited to the following:molecules or ions directly or indirectly detectable based on lightabsorbance, fluorescence, reflectance, light scatter, phosphorescence,or luminescence properties; molecules or ions detectable by theirradioactive properties; molecules or ions detectable by their nuclearmagnetic resonance or paramagnetic properties. Included among the groupof molecules indirectly detectable based on light absorbance orfluorescence, for example, are various enzymes which cause appropriatesubstrates to convert, e.g., from non-light absorbing to light absorbingmolecules, or from non-fluorescent to fluorescent molecules.

Polymerase chain reaction (PCR) has been described in U.S. Pat. Nos.4,683,195, 4,800,195, and 4,965,188, the entire disclosures of which areincorporated by reference herein.

The phrase “obligate carrier” refers to an individual who is aheterozygous carrier of a gene associated with an autosomal recessivedisorder.

A DNA microarray (also commonly known as gene chip, DNA chip, orbiochip) is a collection of microscopic DNA spots attached to a solidsurface, such as glass, plastic or silicon chip forming an array.Exemplary cDNA microarrays of the invention are commercially availableand may be purchased from such companies as Agilent Technologies,Affymetrix Inc. (Santa Clara, Calif.), Nanogen (San Diego, Calif.) andProtogen Laboratories (Palo Alto, Calif.).

Microarrays are used to quantify mRNAs transcribed from differentprotein-encoding genes. RNA is extracted from a cell or tissue sample,then converted to cDNA. Fluorescent tags, (usually Cy3 and Cy5) areenzymatically incorporated into the newly synthesized cDNA or can bechemically attached to the new strands of DNA. A cDNA molecule thatcontains a sequence complementary to one of the single-stranded probesequences on the array will hybridize, via base pairing, to the spot atwhich the complementary reporters are affixed. The spot will thenfluoresce (or glow) when examined using a microarray scanner. Thefluorescence intensity of each spot is then evaluated in terms of thenumber of copies of a particular mRNA, which ideally indicates the levelof expression of a particular gene.

DNA microarrays can be used to detect RNAs that may or may not betranslated into active proteins. This analysis is termed “expressionanalysis” or expression profiling. Since there can be tens of thousandsof distinct reporters on an array, each microarray experiment canaccomplish the equivalent number of genetic tests in parallel.

In a preferred embodiment of the invention, cDNA microarrays wereprepared on aminoalkylsilane coated microscope, slides (Sigma, St Louis,Mo.) using a pin-and-ring arrayer (Affymetrix 417, Bedford, Mass.).

Oligonucleotide microarrays (or single-channel microarrays), are alsocontemplated herein. There, the probes are designed to match parts ofthe sequence of known or predicted mRNAs. There are commerciallyavailable designs that cover complete genomes from companies such asAffymetrix, or Agilent. These microarrays give estimations of theabsolute value of gene expression and therefore the comparison of twoconditions requires the use of two separate microarrays.

Long Oligonucleotide Arrays are composed of 60-mers, and are produced byeither ink-jet or robotic printing. Short Oligonucleotide Arrays arecomposed of 25-mer oligos, and are produced by photolithographicsynthesis (Affymetrix). More recently, Maskless Array Synthesis fromNimbleGen Systems has combined flexibility with large numbers of probes.Arrays can contain up to 390,000 spots, from a custom array design.

Statistical analysis of the expression differential can be performedusing the 1-sample t-test, but also may include other statisticalmethods. For example, the one-class Significance Analysis of Microarrays(SAM) analysis is contemplated as within the scope of the inventivesubject matter.

The following examples provide illustrative methods of practicing theinstant invention, and are not intended to limit the scope of theinvention in any way.

EXAMPLES

Differential gene expression in lymphoblastoid cell lines frommonozygotic twins discordant for classic autism

To determine whether LCL derived from individuals with autism exhibitpatterns of gene expression that may be relevant to autism spectrumdisorders (ASD), gene expression profiling was performed on LCL derivedfrom 3 sets of male monozygotic twins, one of each pair who met standarddiagnostic criteria for autism based on the ADI-R. In each case, theother twin, while not clinically autistic, exhibited autistic traits andwas classified either as “broad spectrum” or “not quite autistic”according to guidelines described by the Autism Genetic ResourceExchange (AGRE) repository (http://www.agre.org). Two of the three twinpairs had an unaffected sibling and these were also used for comparisonwith their respective twin siblings. All of these assays employed anexperimental design in which RNA from twin siblings were cohybridized ontwo-color spotted microarrays containing 39,936 human cDNA elements.Each microarray experiment involved dye-reversal replicates, and wasperformed in duplicate or, in one case, triplicate for the differentsets of twins. The mean log 2 ratios of each gene were used for SAManalyses of the biological replicates.

Principal components analysis (PCA) of the combined microarray data withrespect to samples from the 3 discordant twin sets showed that genotypeis responsible for the major portion of the variation in differentialgene expression, reflecting the expected transcriptome heterogeneityamong unrelated individuals (Supplementary FIG. 1). The microarray datafrom the 3 sets of discordant twins was analyzed using SAM in order toidentify genes that were significantly different from log 2=0 across thebiological replicates (n=3). Twelve hundred genes were identified assignificant with a median FDR of 26%. Twenty-five genes were found to beup-regulated at least 1.5-fold in the more severely affected twinrelative to the other twin (log 2(ratio)≧0.58) and 19 genes weredown-regulated by at least 1.5-fold (Table 1). Of these, nine of the 26known genes (representing seven unique genes) correspond to genesinvolved in neurological development, function, or disease. Because ofthis surprising finding, we used quantitative RT-PCR (qPCR) to confirmthe differential expression of these specific genes. As shown in Table1, qPCR confirmed the relative expression levels of all but one ofthese, including argininosuccinate synthetase (ASS), close homologue ofL1 (CHL1), cell death-associated kinase (DAPK1),5-lipoxygenase-activating protein (ALOX5AP or FLAP), interleukin-6signal transducer (IL6ST), and Roundabout homolog 1 precursor (ROBO-1).

Moreover, when the expression profile of cells from the autistic twinwas directly compared against that of his respective normal sibling in adye-reversal microarray experiment, neurologically relevant genesrepresented 3 of the top 5 most differentially expressed genes (Table2). Interestingly, the mean log 2(ratio) of each of these, ASS, CHL1,and FLAP, are higher for the autistic twin than for the more mildlyaffected twin when each is compared against their respective normalsibling, suggesting a quantitative relationship between differentialgene expression (relative to normal individuals) and severity ofautistic symptoms. These quantitative differences have also beenconfirmed by qPCR analyses.

Network analysis shows overlap of pathways involving differentiallyexpressed, neurologically relevant genes

Pathway analysis using Ingenuity Pathways Analysis Software of thesignificant genes from the SAM analysis further revealed that 25 out of58 network focus genes with log 2(ratio) greater than ±0.58 (±1.5-fold)in at least one discordant twin set are involved in neurologicalfunction or disease (Table 3). This expression cutoff was selectedbecause of reports in the literature that 1.47-fold increases ordecreases in gene expression are generally reproducible when Lowessnormalization is used (Yang et al, 2002), and our own ability to confirmexpression changes of at least 1.5-fold by qPCR. Of particular note isthe gene network that is derived from pathway analysis of the meanexpression values (with log 2 ratio ≧±0.58) across 3 sets of discordanttwins which shows that the majority of significantly differentiallyexpressed genes are part of an extended network centered on TNF (FIG.1). The neurological functions of 7 of these network focus genes aredescribed in Table 3. The collective data from the above-mentionedmicroarray and qPCR studies suggested a short list of novel candidateautism susceptibility genes for further evaluation.

Differential expression of autism candidate genes in “concordant”autistic twins

Expression analysis of autism candidate genes in LCL from two sets oftwins in which both individuals were diagnosed as autistic surprisinglyshowed differential expression of several of the candidate genes (Table4). However, in each case, the “concordant” autistic twin siblings werefound to be discordant with respect to severity of language impairmentbased on each twin's scores on the Peabody Picture Vocabulary Test(PPVT) (see Supplementary Table X for profile of subjects studied).Thus, when microarray data from the more language-impaired twin (lowerpercentile PPVT score) was compared relative to the less impaired twin,the differential gene expression profile was consistent with the resultsobtained from the “discordant” twin sets. This result underscores theimportance of considering autistic phenotype and/or severity as a meansof reducing heterogeneity of gene expression in the search forbiomarkers of autism. Interestingly, as shown in Table 4, expressionanalysis of the candidate genes in cells from monozygotic nonautistictwins demonstrated that two of the genes, CHL1 and ROBO1, weredifferentially expressed. However, it is worth noting that this set of“normal” twins has two autistic siblings. It is therefore possible thatthe differential expression of these two neurologically relevant genesis not coincidental, but does not, by itself, meet the threshold forassociation with an autistic phenotype. Alternatively, this result mightsuggest that these genes are not involved in autism. Clearly, thisobservation on only one set of normal twins warrants furtherinvestigation preferably with twins with no autism in their familybackground, but it is difficult to obtain normal monozygotic twins withno autistic siblings from the AGRE repository which focuses oncollecting samples from pedigrees with familial autism.

The serotonin transporter (5-HTT) gene is also differentially expressedin lymphoblastoid cells from monozygotic twins discordant in severity ofautism and/or language impairment

To evaluate whether differential expression of the serotonin transporter(5-HTT), which is strongly implicated in autism, can be detected in LCLfrom the autistic and nonautistic twins, qPCR analyses were performed,as 5-HTT is not represented on the microarray platform. Resultsindicated that, while there is no difference in 5-HTT expression betweenthe nonautistic twins, there is a significant decrease in expression inthe more severely affected twin in all of the autistic twin pairsstudied, as shown in Table 4. Reduced expression of 5-HTT inblood-derived cells may explain hyperserotonemia in a subset of autisticindividuals [27]. It should also be noted that a polymorphism in thepromoter region of 5-HTT which results in reduced transcription of 5-HTTis a factor in anxiety related traits [28, 29], common in autism. Thepresent finding suggests that LCL, or their precursor blood lymphocytes,may be useful as reporter cells to evaluate neurologically relevant geneexpression differences between autistic and normal individuals.

Network and global functional analyses of the pooled microarray data onmonozygotic twins with autism highlight genes involved in nervous systemdevelopment and function

Because of the observed relationship between severity of symptoms anddifferential expression of candidate genes across the 5 autistic twinpairs studied, SAM was applied to microarray data from all 5 sets oftwins to identify genes that were significantly up- or down-regulatedacross all twin pairs, each pair of which exhibited differentialseverity with respect to language ability (Table 5). Once again, pathwayanalysis of the differentially expressed significant genes revealed anextended network centered on TNF, connecting a number of neurologicallyrelevant genes (FIG. 2). Global functional analysis of the significantdifferentially expressed genes from 5 pairs of twins further shows thatgenes related to “nervous system development and function” are among themost statistically significant, enriched genes across the 5 sets oftwins (Table 6).

In silico mapping of differentially expressed genes to chromosomalregions containing autism candidate genes or quantitative trait loci(QTLs)

Although most of the differentially expressed genes identified in thisstudy are novel candidate genes with respect to autism, Table 7 showsthat 6 out of 8 of the novel candidate genes listed in Table 4 [andapproximately half of the differentially expressed genes listed inTables 3 and 5 (Supplementary Table 1)] map to chromosomal regionscontaining previously reported autism candidate genes or recentlyidentified QTLs for spoken language and nonverbal communication. Thisobservation is interesting in that the overlay of expression data ontogenetic data allows us to focus on expressed, neurologically relevantgenes that may relate to the functional phenotype. Taken together withthe network and global functional analyses described above, theseresults present a compelling argument for further investigation ofblood-derived cells as surrogates to identify biomarkers for autism.

DISCUSSION

These studies show, for the first time, that candidate genes for autismmay be expressed in peripheral cell lines derived from individuals withASD. This observation is a critical step towards development of adiagnostic screen for autism based on biomarker detection in an easilyaccessible tissue (i.e., blood)

In this study, DNA microarrays containing ˜40K human cDNA probes wereutilized to examine differences in gene expression profiles in LCLderived from 5 pairs of monozygotic twins with ASD. Three sets of twinswere discordant with respect to clinical diagnosis of autism, and 2 sets(both diagnosed as autistic) differed with respect to severity oflanguage impairment. The most remarkable finding of this study is thatglobal functional analysis of the significant differentially expressedgenes in LCL from these 5 sets of twins identifies “Nervous systemdevelopment and function” as a top “high level function” that issignificantly enriched within the gene datasets (Table 6). Moreover, insilico mapping of our gene expression data demonstrates that many of thedifferentially expressed genes are located in or close to chromosomalregions previously identified as autism susceptibility loci by geneticanalyses (Table 7 and Supplementary Table 3). Quantitative RTPCRanalysis has further confirmed the differential expression of a subsetof our novel candidate genes in the majority of twin sets studied.Several of these candidate genes and their associated gene networks mayprovide insight into potential mechanisms involved in the autisticphenotype(s). One of the striking results of the pathway analyses isthat a relatively large number of the differentially expressed,neurologically relevant genes are linked in networks that are centeredon genes involved in inflammation (see FIGS. 1 and 2): These include theproteins ASS, ALOX5AP (FLAP), DAPK1, F13A1, IL6ST, NAGLU, and ROBO1. Theprotein ASS regulates the rate-limiting step involved in nitric oxide(NO) production through regeneration of arginine from citrulline, abyproduct of the nitric oxide synthetase (NOS) reaction [30]. Since NOis a major signaling molecule in the brain that has been implicated inseveral psychiatric disorders, including autism [31], the increasedexpression of ASS may be of potential relevance to the autisticphenotype. ASS has also been shown to be induced in a rat model of braininflammation [32], which would be consistent with the hypothesis thatneural inflammation may play a role in autism [33]. DAPK1, a celldeath-associated serine/threonine kinase which is involved insuppression of integrin activity and disruption of matrix survivalsignals [34], is also induced by inflammation [35]. Furthermore, thefact that IL6ST (gp130) is increased in LCL from the more severelyaffected twin, may complement previous observations that IL-6 is themost elevated inflammatory cytokine in the middle frontal gyms andanterior cingulate gyms of brain autopsy tissue from autisticindividuals [33]. While upregulation of ASS, DAPK1, and IL6ST may beresponses to inflammation, ALOX5AP (FLAP) mediates inflammation throughactivation of 5-lipoxygenase which is involved in leukotriene production[36]. Interestingly, 5-lipoxygenase has been implicated in aging andneurodegenerative diseases [37], as well as other psychiatric disorders[38], including anxiety and depression, which are frequently co-morbidconditions of autism. Collectively, the involvement of these specificgenes that are associated with neurological function and disease andtheir presence in pathways regulated by inflammatory mediators lendfurther support to the neural inflammation model for autism.

In addition to the possible role of genes involved in inflammation, areview of the gene list in Table 3 suggests several additional recurringthemes among the differentially expressed genes with neurologicalfunctions: neuronal survival, neurite extension/guidance, andmyelination. In this regard, altered expression of EGR2 may beparticularly significant. EGR2 (Krox-20) is a transcription factorinvolved in the development of the brain and peripheral nervous system,routing of axons, and myelination [39]. Some of these functions may berelated to EGR2-mediated regulation of ROBO1, which is involved inneuronal differentiation and axon guidance [40, 41], and integrin beta-7(ITGB7) which has been implicated in chronic demyelinating disease [42].The expression levels of all three of these genes are relatively reducedwith increased severity of autism or language impairment. Theinvolvement of cell migration and survival in the pathophysiology ofautism is also implicated by the higher expression level of CHL1, anovel neural cell adhesion molecule that is involved in neuritemigration, outgrowth, connectivity, and survival, which is associatedwith the more autistic phenotype. Deficiency in CHL1 has been shown tobe associated with mental and motor impairments as well as withalterations in exploratory and emotional behavior in mice [43, 44],characteristics that are often associated with autism. However, theeffect of CHL1 overexpression has yet to be determined. While thefunction of such neurologically relevant genes in lymphoblastoid celllines is unknown, if expression of these genes can be shown to beconsistently altered in LCL, these cells, and by inference theirprecursor blood lymphocytes, can potentially be used as reporter cellsfor diagnosis of ASD.

The observed relationship between differential gene expression andseverity of ASD between monozygotic twins suggests a role for epigeneticfactors in ASD. Indeed, epigenetic differences between monozygotic twinshave been examined as possible causes for discordancy in schizophreniaas well as bipolar disorder [46-48]. A recent report on normalmonozygotic twins indicates that epigenetic differences arise over time,increasing with age and with separation from each other after birth[45]. Possible epigenetic mechanisms leading to differences in geneexpression include differential methylation, differences in histoneacetylation, and micro RNA, although there is no available evidencelinking any of these to autism at this time. On the other hand, amutation in a methyl-CpG binding protein, X-linked MeCP2, has beenidentified as being involved in 80% of all cases of Rett Syndrome, adevelopmental disorder which overlaps ASD, thus implicating theimportance of methylation-dependent gene expression in at least thisrelated disorder. One could therefore postulate that differentialmethylation or differential histone acetylation might give rise todifferential expression in LCL from monozygotic twins with ASD and testfor global changes in methylation or histone acetylation as done byFraga et al [45]. If present, these differences could, in turn, be theresult of stochastic processes, environmental factors, or theimmortalization procedure used to generate cell lines from primarylymphocytes. The latter possibility could be further tested byevaluation of the methylation/acetylation patterns of DNA/histones inprimary lymphocytes from monozygotic twins discordant in severity ofautism or language impairment within autism which, while interesting, isoutside of the scope of this study. Regardless of origin, the geneexpression differences between monozygotic twins who present withdifferential severity along the autism spectrum or within a specificdomain (eg., language) are potentially useful, not only as biomarkersfor ASD, but also as indicators of genes or metabolic/signaling pathwaysthat may contribute to the autistic phenotype. While our short list ofcandidate genes focuses on genes that are similarly differentiallyexpressed between twin sets, the set of differentially expressed,neurologically relevant genes that are unique to a given twin set mayalso be important to the determination of a specific autistic phenotype.Inasmuch as our microarray analyses directly compared geneticallymatched individuals who differ only in degree of expression of autisticsymptoms, it is likely that other genes, not identified in our study,also play a role in the etiology and pathophysiology of autism. Thisexperimental design possibly explains why the candidate genes identifiedhere are different from those reported by an earlier genomic study [20]which compared autopsy brain tissues from autistic and normal(nonautistic) controls (i.e., case-control studies). On the other hand,it is interesting that many of our novel genes map to geneticallyidentified autism susceptibility loci or QTLs (Table 7 and SupplementaryTable 1).

Aside from identifying novel candidate genes for autism, our study alsodemonstrates the need for phenotype definition or subgrouping accordingto severity along a specific behavioral domain for biological studies ofautism. Specifically, the results show that the differential geneexpression profiles of concordantly autistic twins with differentialseverity of language impairment mirror some of the differences in geneexpression which are observed in the twins with discordant diagnosis ofautism, who also exhibit differential language deficits. Thus, forcase-control studies in which individuals from the general populationare compared against unrelated controls, subgrouping the autisticindividuals by phenotype or stratifying them according to severity ofsymptoms may provide more clarity in analyzing biological data. Towardsthis goal, we have used several different clustering methods to divideover 1300 autistic individuals into endophenotypic subgroups (eg.,language, nonverbal communication, and savant skills) based on itemscores on the ADIR questionnaire (manuscript in preparation). Based onthese methods, the twin siblings analyzed in this study each fall intodifferent clusters in all but one case involving a discordant twin set.These “endophenotypic” differences may therefore account for some of thedifferences in gene expression profiles between the siblings as well asamong the different sets of twins.

CONCLUSIONS

In summary, this data indicates that LCL from genetically identicalautistic individuals who differ in severity of autistic symptoms and/orlanguage impairment exhibit differential expression of genes relevant toneurological development, structure, and function. Many of these genesmap to regions previously identified by genetic analyses as harboringautism susceptibility genes or QTLs, demonstrating the power of combinedgenomic-genetic analyses to prioritize autism candidate genes forfurther study. In addition, a quantitative relationship is seen betweenseverity of symptoms and expression of several autism candidate geneswhen twins with classic autism or with milder autistic traits arecompared against their respective normal siblings. The finding that geneexpression differences were also observed in cells from twins who wereboth diagnosed as autistic, but who differed in severity in languagedeficits strongly suggests that autistic phenotype as well as severityof symptoms must be considered in gene expression studies on autisticindividuals in order to reduce biological heterogeneity due to thesefactors. Collectively, these studies provide proof-of-principle that LCL(or peripheral blood cells) may exhibit biomarkers relevant to autism,and further suggest their potential usefulness as reporter cells indeveloping a diagnostic screen for autism. While it is unlikely thatmicroarray studies on LCL will identify the etiology(ies) of autism,this global approach to gene expression is expected to highlightmolecular or pathway defects related to the pathophysiology of thecondition which can be targeted for drug therapies. Moreover, as opposedto fixed autopsy tissues in which RNA may have degraded, a live cellmodel can be used to examine the functional consequences of the genomicalteration(s) and the efficacy of different pharmacological agents inameliorating the impaired function.

Materials and Methods

Cell Lines and Culture Conditions

Lymphoblastoid cell lines (LCL) derived from lymphocytes of 5 pairs ofmonozygotic twins with ASD were obtained from the Autism GeneticResource Exchange (AGRE; Los Angeles, Calif.) and cultured in DMEM with15% fetal bovine serum and 1% penicillin-streptomycin. Cell lines fromnormal siblings of 2 sets of twins were also obtained for comparison ofgene expression profile with that of their respective autistic siblings.In addition, cell lines from a set of non-autistic monozygotic twinswere also studied. To minimize differences in gene expression due toculture and sample workup conditions, all samples that underwent directcomparison of gene expression profile were cultured and harvested at thesame time using the same medium preparation and RNA isolation reagents.

Description of Individual Donors of Cell Lines

Supplementary Table 2 provides a case description of all of the subjectsincluded in this study. In brief, all of the twin pairs and normalsiblings, with the exception of 1 set of twins, were Caucasian malesbetween the ages of 6 and 16 at the time that blood was drawn. Theremaining set of twins (age 12) was of mixed race (black, Hispanic) buthad the same mother as one of the Caucasian pairs of autistic twins. For3 sets of twins (designated “discordant” twins), one twin of each pairmet standard diagnostic criteria for autism based on the AutismDiagnostic Interview-Revised (ADIR) [13]. In each case, his co-twin,while not clinically autistic, exhibited autistic traits and could beconsidered to be on the autism spectrum. These co-twins were describedeither as “Broad spectrum” or “Not quite autistic (NQA)” by the AGRErepository according to criteria established on the basis of ADI-Rscores. Gene expression in cell lines from two of these twin pairs werealso directly compared against the gene expression profile in cell linesfrom their respective “normal” sibling. Two of the 5 sets of twins withASD (designated “concordant” twins) were examples in which both co-twinswere diagnosed with autism, but who were discordant in severity oflanguage impairment, as indicated by their respective percentile scoreson the Peabody Picture Vocabulary Test (PPVT). The Autism DiagnosticObservation Schedule (ADOS) [14] was used to diagnose one of these setsof twins. None of the individuals whose cells were used presented withany co-morbid condition or mental retardation. All of the phenotypicdata were obtained through the AGRE databases (www.agre.org).

DNA Microarray Analyses

RNA was isolated from the LCL using TRIzol Reagent (Invitrogen, CA)according to the manufacturer's protocol. The RNA was further purifiedusing Centricon-X columns and tested for purity on RNA 6000 NanoChipsusing the Agilent 2100 Bioanalyzer. Labeled cDNA was obtained byincorporation of 5-(3-aminoallyl)-2′ deoxyuridine-5′-triphosphate(Ambion, Tex.) during first-strand synthesis, followed by coupling tothe ester of cyanine (Cy)-3 or Cy-5 (Molecular Probes, OR) asappropriate according to Standard Operating Protocol (SOP) M004 on TheInstitute for Genomic Research (TIGR) website (http://www.tigr.org). Fortwo-color microarray analyses, the Cy5- and Cy3 labeled cDNA from eachpair of twins (or twin and normal sib) were co-hybridized using TIGR SOPM005 to spotted microarrays (TIGR 40K Human Set) containing 39,936 humancDNA probes which were obtained from Research Genetics. Dye reversal(flip-dye) replicates were included in all analyses, and at least 2 setsof replicates were carried out for each pair of monozygotic twins. Geneexpression levels were derived from the scanned hybridized arrays usingTIGR SpotFinder; MIDAS, and MeV analysis programs which are all part ofthe TM4 Microarray Analysis Software Package available at theabove-cited website. These programs have all been previously describedin detail [49]. Data analyses included normalization using local LOWESSfollowed by standard deviation regularization across individualsubarrays, and flip-dye consistency checking for dye reversal replicatesas implemented in MIDAS [50, 51]. The SAM (Significance analysis ofmicroarrays) module within MeV was used to determine statisticalsignificance of differential expression and false discovery rates (FDR)for genes from biological replicates.

Quantitative RT-PCR

Total RNA was reverse transcribed into cDNA using the iScript cDNASynthesis Kit (Bio-Rad, Hercules, Calif.). Briefly, 2 μg of RNA wereadded to a 40 μl reaction mix containing reaction buffer, magnesiumchloride, dNTPs, an optimized blend of random primers and oligo(dT), anRNase inhibitor, and a MMLV RNase H+ reverse transcriptase. The reactionwas incubated at 25° C. for 5 minutes followed by 42° C. for 30 minutesand ending with 85° C. for 5 minutes. The cDNA reactions were thendiluted to a volume of 100 μl with water. Real-time PCR was carried outon a 7900HT Sequence Detection System from Applied Biosystems using theiTaq SYBR Green Supermix with ROX (Bio-Rad, Hercules, Calif.).Gene-specific primers at a final concentration of 200 nM and 1 μl ofcDNA templates were combined into 20 μl reaction mixes and run through40 cycles of PCR. Quantitation was performed using the Universal 18SrRNA primers (Ambion, Austin, Tex.) with samples normalized to their 18SrRNA standard curves. Forward and reverse primers are described inSupplementary Table 3.

Network Prediction Analyses

Lists of differentially expressed genes identified as “significant” bythe 1-sample t-test on microarray data across different sets of twinswere analyzed using Ingenuity Pathways Analysis (Ingenuity Systems,Inc.), a web-delivered application that enables biologists to discover,visualize and explore therapeutically relevant networks significant totheir specific experimental results (e.g., gene expression array datasets). Specifically, a data set containing gene identifiers (in thiscase, GenBank Accessions) and their corresponding expression values wereuploaded as an Excel spreadsheet using the template provided in theapplication. Each gene identifier was mapped to its corresponding geneobject in the Ingenuity Pathways Knowledge Base. The gene list wasfiltered prior to analysis with Ingenuity by using a log 2 cutoff of0.58. These genes were then used as the starting point for generatingbiological networks. The networks are displayed graphically as nodes(genes/gene products) and edges (the biological relationships betweenthe nodes). Human, mouse, and rat orthologs of a gene are stored asseparate objects in the knowledge base, but are represented as a singlenode in the network. The intensity of the node color indicates thedegree of up-(red) or down-(green) regulation. When networks fromdifferent samples are merged, yellow node color denotes overlappingdifferentially expressed genes from two or more samples. Nodes aredisplayed using various shapes that represent the functional class ofthe gene product. (See Ingenuity's website [http://www.ingenuity.com]for shape legend)

Global Functional Analyses

Biological functions were assigned to the overall analysis (across datafrom 5 monozygotic twin pairs) by using the findings that have beenextracted from the scientific literature and stored in the IngenuityPathways Knowledge Base. The biological functions assigned to theanalysis are ranked according to the significance of that biologicalfunction to the analysis. A Fisher's exact test is used to calculate ap-value determining the probability that the biological functionassigned to the analysis is explained by chance alone.

in Silico Mapping of Differentially Expressed Genes

The physical locations of each of the significant differentiallyexpressed genes with log 2 ratio ≧±0.58 were obtained using TIGR'sResourcerer Gene Annotation Software. These locations were then comparedmanually to those of autism candidate genes (ACG) or quantitative traitloci (QTLs) identified on the basis of genetic linkage and associationstudies.

Systems for Analysis of Arrays

In an embodiment, the present invention provides systems for carryingout array analysis. Thus, in an embodiment, the present inventioncomprises a computer-readable medium on which is encoded programmingcode for analyzing gene expression from a plurality of data pointscomprising a gene list which is filtered using a log 2 cutoff of 0.58.

Also in an embodiment, the present invention may comprise acomputer-readable medium on which is encoded programming code foranalyzing gene expression comprising code for: (a) determiningcross-correlation between at least two genes within a group, whereincross-correlation indicates a gene involved with neuronal developmentand function.

Embodiments of computer-readable media include, but are not limited to,an electronic, optical, magnetic, or other storage or transmissiondevice capable of providing a processor with computer-readableinstructions. Other examples of suitable media include, but are notlimited to, a floppy disk, CD-ROM, magnetic disk, memory chip, ROM, RAM,an ASIC, a configured processor, all optical media, all magnetic tape orother magnetic media, or any other medium from which a computerprocessor can read instructions. Also, various other forms ofcomputer-readable media may transmit or carry instructions to acomputer, including a router, private or public network, or othertransmission device or channel, both wired and wireless. Theinstructions may comprise code from any computer-programming language,including, for example, C, VISUAL C#®, VISUAL BASIC®, VISUAL FOXPRO®,Java, and JavaScript.

As described above, the system may comprise an imaging unit as well as ameans for the user to interact with the system as the analysis proceeds.Thus, in an embodiment, the present invention further comprises a unitfor collecting and/or compiling data from said plurality of measuredsignals and transmitting said data to said computer, and a unit fortransmitting the results of said analysis to a user.

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These references are incorporated herein in their entirety, particularlyas they relate to teaching the level of ordinary skill in this art andfor any disclosure necessary for the commoner understanding of thesubject matter of the invention as defined by the claims.

It will be clear to a person of ordinary skill in the art that the aboveembodiments may be altered or that insubstantial changes may be madewithout departing from the scope of the invention. Accordingly, thescope of the invention is determined by the scope of the followingclaims and their equitable Equivalents.

TABLE 1 Significant up- and down-regulated genes from SAM analysis ofmicroarray experiments on 3 sets of monozygotic twins discordant forautism diagnosis with log2(ratio) ≧± 0.58 Genbank # Gene name ordescription Mean log2(ratlo)* Obs. d score^(Δ) qPCR^(¥) Uprequlated(log2(ratio) ≧ 0.58) R45254 Unknown protein, 1.19 2.95 AA448599 F13A1,clotting factor XIIIa precursor 1.08 2.26 AA676466 ASS,argininosuccinate synthetase (aa 1-412) 0.92 5.92 AA992985 Unknownprotein 0.88 2.38 AA676405 ASS, argininosuccinate synthetase (aa 1-412)0.85 4.56 1.47 W07099 NAGLU, N-acetylglucosaminidase, alpha 0.85 2.24−0.28 AA044267 P2X5a 0.80 2.36 T49652 FLAP, ALOX5AP 0.79 1.88 1.01H57830 histone H1(0) (aa 1-194) 0.78 4.53 R00276 CD38 alt 0.76 1.45AA488070 Unknown protein 0.75 1.63 W69399 histone H1(0) (aa 1-194) 0.747.15 H09567 PAG1 0.74 1.97 AA609189 Unknown protein 0.73 1.87 H02307FLAP, ALOX5AP 0.70 1.79 N50114 PAG1 0.69 2.30 Al091671 Unknown protein0.67 2.14 N70181 PLEKHG1 0.66 2.85 H24011 Homeodomain-like protein 0.641.66 AA412520 Unknown protein 0.63 1.97 AA521362 CR2 receptor 0.62 1.80Al371096 DAPK1, death-associated protein kinase 1 0.62 1.58 0.65 T61343IL6ST, IL6 signal trariducer, gp130 0.59 1.91 0.58 N29918 ZBTB10 0.591.55 T90067 EIF2C2 0.59 3.24 Downrequlated (log2(ratio) ≦ −0.58) T67053IGLC2 −2.39 −5.93 W73790 IGLL1 −2.00 −8.34 H18423 Unknown protein −1.98−8.42 AA448157 CYP1B1 −0.95 −2.38 AA644099 Unknown protein −0.89 −1.73AA933744 ECAT11 −0.84 −1.97 Al018127 Unknown protein −0.83 −2.26AA451886 CYP1B1 −0.77 −2.90 AA682565 Unknown protein from neuroblastoma−0.72 −3.06 Al223429 Unknown protein −0.69 −2.37 AA450353 ELMOD1 −0.69−1.87 AA873578 IGHG1 −0.67 −2.29 R33402 SAMSN1 −0.67 −1.92 AA173755ROBO1, roundabout 1 −0.66 −3.00 −0.93 AA022886 retinal degeneration Bbeta −0.64 −2.70 AA063573 SAMSN1 −0.64 −1.74 H99699 mitochondrialaconitase −0.63 −2.79 Al290663 CYBASC3 −0.60 −3.20 AA449333 Rab22b −0.58−1.71 * Mean log₂(ratio) of gene expression in lymphoblastoid cell linesfrom children exhibiting classic autism to cell lines from less affectedmonozygotic twin sibs. ^(Δ)Observed d score from SAM analysis for whichmedian FDR was 26.4%. ^(≠)Mean log₂(ratio) of qPCR data from 3 sets ofmonozygotic twins. Each gene was analyzed in triplicate and the meanlog₂(ratio) for each respective gene was averaged among the 3 sets. Onlyone gene, NAGLU, was not confirmed by qPCR, possibly because ofsuboptimal choice of primers for qPCR. Genes in boldface type have beenshown to be relevant to neurological development, structure, or function(See Table 3).

TABLE 2 Expression of ASS, CHL1, and FLAP in 2 sets of discordantmonozygotic twins relative to expression levels in their respectivenormal siblings Gene name Genbank # A1 M1 A2 M2 ASS AA676405 0.65(0.81)* −0.13 (0.27) 1.77 (3.04) 0.18 (0.97) CHL1 H15267 1.60 (1.64)0.67 (1.39) 1.15 (0.78) 0.95 (0.60) FLAP T49652 0.71 (1.10) 0.13 (0.77)1.40 (1.45) −0.09 (−0.39) Values are mean log₂(ratio) measures of geneexpression between a twin and his respective normal sibling, obtained byDNA microarray analyses with dye reversal replicates * Values inparentheses are mean log₂(ratio) measures obtained by triplicate qPCRanalyses. A = autistic twin as diagnosed by ADI-R scoresheet M = moremildly affected twin who did not meet ADI-R criteria for autism

TABLE 3 Network focus genes from Ingenuity Pathways Analysis withlog₂(ratio) >± 0.58 GenBank # Gene Neurological function or diseaseUpregulated T49652 ALOX5AP neuronal signaling; possbly neurodegenerativediseases AA991590 APOC1 AA147170 ALS4 ataxia-ocular apraxia AA676466 ASSinvolved in nitric oxide production H21041 ATF3 extension of neuritesAA702350 AUTS2 Asperger's syndrome Al341427 BCAT1 AA430367 CBS R00276CD38 AA283949 CDC14A N67039 CDK6 H15267 CHL1 extension of neurites;organization of mossy fibers AA521362 CR2 AA884403 CTF1 myelination,differentiation of neurons Al371096 DAPKI apoptosis of hippocampalneurons W00789 DST coalignment of neurofilaments, projection of axons;dysmyelination AA448599 F13A1 stroke AA149640 FLT1 VEGF-induced releaseof nitric oxide AA070902 GGA2 Al375302 HMGB1 extension of neuritesAl539460 IL7 AA406546 IL6ST myelination, development of motor neurons,retraction of dendrites H09062 MLSTD1 W07099 NAGLU neurogenesis;vacuolation of neurons AA598611 NR4A2 neurogenesis; metabolism ofdopamine AA707195 NTRK2 survival of Purkinje cells; apoptosis of neuronsAA044267 P2RX5 H09567 PAG possible role in chronic neuroinflammationAA972337 PAWR AA489629 PBEF1 Al016039 PLXNB2 R80217 PTGS2 activation ofastrocytes; spatial memory in mice; apoptosis of neurons AA495950 RRM2BR27457 SLC38A2 Al091460 SOS1 N63153 SPRED1 Al040821 TERE1 AA970358 TSLPDownregulated AA779727 ADAM19 development of septum R01732 AMPD3AA478589 APOE quantity/morphology of neurons; neurite extension;learning in mice AA984646 C7orf2 AA448157 CYP1B1 AA446027 EGR2myelination; development of motor neurons; routing of axons AA149096 HCKAA620511 HSPA8 W73790 IGLL1 Al380522 ITGB7 chronic demyelinating diseaseAA679503 KIF1B morphology and size of brain; neuron survival AA029283LARGE T83159 LSP1 Al351740 LTB neurological disorder in rats AA022886PITPNC1 Al126054 PTK2 AA173755 ROBO1 axon guidance AA457700 SCD neuralregeneration AA504211 TNFSF11 N68465 UAP1 Dataset included genes derivedfrom SAM test across three twin sets that met expression cutoff in atleast one of the twin sets. Genes in Boldfaced type are ones ofneurological relevance.

TABLE 4 Relative expression of candidate genes in monozygotic“concordant” twin pairs with differential language impairment (PPVTpercentile scores) and in normal twins Candidate Gene Genbank #PPVT-30/42* PPVT-0.1/1 Discordant twins^(¥) Normal twins ASS AA6764050.03 (−0.26)^(¶) −0.01 (−0.69) 0.85 (1.37) 0.24 (0.54) CHL1 H15267 1.83(1.48) 1.99 (1.29) 0.61 (0.46) 1.40 (1.45) IL6ST T61343 0.88 (1.33) 0.28(−0.26) 0.58 (0.49) 0.37 (0.23) IL6ST AA406546 1.02 (0.85) 0.34 (−0.14)0.58 (0.61) 0.43 (0.47) DAPK1 Al371096 −0.56 (−0.92) −0.49 (−1.05) 0.70(0.57) −0.13 (−0.18) FLAP T49652 1.18 (1.20) 0.28 (−0.25) 0.71 (0.58)−0.19 (−0.34) ITGB7 Al380522 −1.12 (−1.13) −0.20 (−0.92) −0.60 (−0.76)0.15 (0.04) EGR2 AA446027 −2.02 (−3.10) −1.26 (−2.16) −0.43 (−0.79)−0.23 (−0.37) ROBO1 AA173755 −0.13 (0.25) 0.41 (−0.18) −0.66 (−1.10)−0.45 (−0.80) 5-HTT^(¶) BC069484 NP^(¶) (−2.39) NP (−0.42) NP (−0.96) NP(−0.02) *Values are mean log2 ratios of gene expression from DNAmicroarray data from 2 sets of monozygotic autistic twins who both metcriteria for autism by either ADOS or ADI-R diagnostic tests, but havedifferential language impairment as indicated by their respective PPVTpercentile scores. Data from 2 separate dye-reversal microarrayexperiments were averaged for each twin set. For each pair of twins,microarray data from the twin with the lower PPVT score was used as thenumerator in calculating the gene expression ratio. PPVT-30/42 refers tothe twin pair whose PPVT percentile scores are 30 and 42, whilePPVT-0.1/1 refers to the twin pair whose percentile scores are 0.1and 1. Interestingly, the two sets of twins share the same mother. ThePPVT-30/42 set are Caucasian males, as are the 3 sets of discordanttwins, while the more severely language-impaired twins (PPVT-0.1/1) areblack, Latino males. ^(#)Values in parentheses are mean log₂(ratio)expression measures obtained by triplicate qPCR analyses. ^(¥)Meanexpression value across 3 sets of twins discordant for diagnosis ofclassic autism. ^(¶)Not present (NP) on microarray

TABLE 5 Significant genes with mean log₂(ratio) ≧ 0.58 across 5 sets oftwins with ASD Mean log₂ Obs. d Genbank # Gene name or description(ratio)* score^(Δ) Unregulated (log2(ratio) ≧ 0.58) AA448599 F13A1,clottin,g factor Xllla precursor 1.50 3.15 H15267 CHL1, neural celladhesion molecule 1.10 2.82 AA521362 CR2 receptor 1.07 2.21 R00276 CD38alt 0.83 2.10 W07099 NAGLU, N-acetylglucosaminidase, 0.77 3.77 alphaT49652 FLAP, ALOX5AP 0.77 2.85 AA044267 P2X5a 0.76 3.22 R40400 CHL1,neural cell adhesion molecule 0.75 2.25 H09567 PAG1 0.71 2.64 Al400399CYP7B1 0.70 2.01 AA149640 FLT1 0.67 3.61 H17800 Unknown protein 0.673.06 H02307 FLAP, ALOX5AP 0.67 2.71 AA917693 Unknown protein 0.66 2.64Al017382 ATXN7L1 0.66 1.95 Al391671 Unknown protein 0.65 2.74 N50114PAG1 0.65 2.82 H95977 Nmd protein, PLA1A 0.65 1.97 AA040389 Unknownprotein 0.64 2.58 H24011 Homeodomain-like protein 0.64 2.71 Al275120Unknown protein 0.63 2.59 AA708955 SCHIP1, schwannomin interacting 0.622.07 protein 1 AA406546 IL6ST, IL6 signal transducer, gp130 0.62 3.19R79082 PTPRK 0.59 2.24 Al241341 CHL1, neural cell adhesion molecule 0.592.41 T61343 IL6ST, IL6 signal transducer, gp130 0.59 3.07 Downregulated(log2(ratio) ≦ −0.58) AA446027 EGR2, Krox-20 homolog −0.90 −2.41AA630734 seryl-tRNA synthetase −0.86 −2.15 R47893 CCL3L1 −0.80 −2.14AA682565 Unknown protein from neuroblastoma −0.76 −3.03 R78530 COTL1−0.73 −2.55 AA933744 ECAT11 −0.73 −2.52 N58443 GPR55 −0.68 −3.04 H99699mitochondrial aconitase −0.64 −4.86 H03494 CD44 −0.63 −2.42 AA450353ELMOD1 −0.63 −2.86 AA458965 IL32, natural killer cell protein, −0.63−2.87 transcript 4 R33402 SAMSNI −0.62 −3.29 AA111969 CD83 antigen −0.60−2.55 Al380522 ITGB7,integrin beta-7 subunit −0.60 −2.51 AA682637 CHST2−0.59 −2.85 * Mean log₂(ratio)of gene expression across 5 sets of twinswith ASD. SAM analysis revealed 1281 significant genes with a median FDRof 15.6%. Genes in boldface type have been shown to be relevant toneurological development, structure, or function.

TABLE 6 Global Functional Analysis: Enrichment of high level functionsrepresented in datasets of differentially expressed genes across 5 setsof monozygotic twins Twin Sets 361/360 809/810 2369/2363 2596/25962597/2595 High Level Function Significance* Significance SignificanceSignificance Significance Nervous system development 0.008-3.85 × 10⁻²0.12-2.55 × 10⁻² 0.81-4.79 × 10⁻² 0.12-4.39 × 10⁻² 0.02-1.53 × 10⁻² andfunction Tissue morphology 0.008-4.27 × 10⁻² NA 0.81-4.79 × 10⁻²0.08-4.38 × 10⁻² 0.51-4.03 × 10⁻² Cell death  0.01-4.27 × 10⁻²   3.74 ×10⁻² 0.09-4.79 × 10⁻² 0.18-4.65 × 10⁻² 0.09-4.53 × 10⁻² Cellulardevelopment  0.01-4.27 × 10⁻² NA 0.81-4.79 × 10⁻² 0.12-4.30 × 10⁻²0.03-3.54 × 10⁻² Immune and lymphatic system  0.03-3.85 × 10⁻² NA0.81-4.79 × 10⁻² 0.33-4.39 × 10⁻² 0.19-4.03 × 10⁻² development andfunction Global functional analysis of-differential gene expres,sionacross 5 sets of monozygotic autistk twins (identified by blood satanicnumbers (eg., 361/360) who are discordant with respect to severity ofautism or language impairment was performed using Ingenuity's PathwaysAnalysis Software. *Significance calculated for each function is anindicator of the likelihood that the high level function is associatedwith the dataset by random chance. The p-value, which is calculatedusing the right-tsiled Fisher's Exact Test, compares the number ofuser-specified genes of interest (in this case, differentially expressedgenes with a log₂(ratio) cutoff ≧± 0.58) that participate in a givenfunction or pathway to the total number of occurrances of these genes inall functional/pathway annotations stored in the Ingenuity PathwaysKnowledge Base. It is noteworthy that genes related to nervous systemdevelopment and function rank first among the top 5 out of 74 high levelfunctions identified in lymphoblastoid cell lines on the basis ofdifferentially expressed genes across 5 sets of twins with autismspectrum disorders. The range of significance values for each high levelfunction relates to the different significance values for specificsubfunctions within the category. NA: no significance value for thisfunction

TABLE 7 Differentially expressed candidate genes from microarrayexperiments mapped in silica to autism susceptibility genes and QTL.Candidate gene Genebank # Physical location Reported closely mappedautism candidate genes or QTL* Rotl ASS AA676405 chr9(130,349,882-130,406,214) dopamine beta-hydroxylase (9q34) 53 CHL1H15267 chr9 (423,533-426,095) KIAA0121 (3p25.2) 54 IL6R-beta, go130T61343 chr5(55,267,950-55,272,765) Spoken language QTL chr5:40(0-67) 52IL6ST AA406546 chr5 (55,271,809-55,272,305) Spoken language QTLchr5:40(0-67) 52 DAPK1 Al371096 chr9 (87,552,642-37,553,099) FLAP,ALOX5AP T49552 chr13 (30,207,643-30,236,932) AUTS3 (13q14-22). HTR2A-2(serobnin rocco. 2A) (13q14-21) 57,58 ITGB7 Al380522 chr12(51,871,361-51,887,333) arginine vasopressin receptor IA (12q14-15) 60EGR2 AA446027 chr10 (64,241,755-64,246,031) Spoken language QTLchr10:107(72-126); HTR-7 52 ROBO1 AA173755 chr3 (76,729,082-78,729,496)

SUPPLEMENTARY TABLE 2 Case description of subjects from whom LCL werederived and used in this study. PPVT Individual ID Blood ID EthnicityZygosity Age* Status (% lle) Raven AU002704 HI0361 Caucasian MZ 8 Autism108 AU002703 HI0360 Caucasian MZ 8 Br. Spec. 79 (8) 105 AU057904 HI0609Caucasian MZ 6 Autism 35 (0.1) 83 AU057905 HI0810 Caucasian MZ 6 Br.Spec. 117 (87) 110 AU057903 HI0813 Caucasian 10 nonautistic AU0885303HI2369 Caucasian MZ 16 Autism No data No data AU0885302 HI2368Caticasian MZ 16 NQA ″ ″ AU0885304 HI2357 Caucasian 19 nonautistic ″ ″AU0616301 HI2595 Caucasian MZ 15 Autism^(¥) 92 (30) 104 AU0616302 HI2596Caucasian MZ 15 Autism^(¥) 97 (42) 94 AU0616303 HI2597 Mixed, MZ 12Autism 40 (<0.1) 80 Hispanic AU0616304 HI2598 Mixed, MZ 12 Autism 66 (1)107 Hispanic AU1165305 HI2745 Caucasian MZ 9 nonautistic AU1165306HI2744 Caucasian MZ 9 nonautistic *Age at time of inclusion in study^(¥)Diagnosed with ADOS rather than ADIR

SUPPLEMENTARY TABLE 3 Primers used for quantitative RT-PCR analyses GeneName GenBank # Forward Primer (5′ to 3′) Reverse Primer (5′ to 3′) ASSAA676405 GAAGTGCGCAAAATCAAACA CTGCACTTTCCCTTCCACTC CHL1 H15627TTTAGATGCACCCGTGTTTG AGCACACCAACATTTCTCATT DAPK1 AI371096CGCTACCTCTCTGTCCCTTG AGGATTCCCTTCTCCCCTTT EGR2 AA446027CCCATCACAGGTTTTTGACC TCTTTTTGCTGTCCCCACTT FLAP T49652GACGATCTCCACCACCATCT AGAATGCTCTCAAGAGCTGAA IL6ST T61343TTAAAAGGTGGCAGCTCAGG TCATCACACGACCCATCAAC IL6ST AA406546GCTGGGCTCATGTAGTTATGG CATCAGAGTGGCTTAGGGACA ITCB7 AI380522CATCACGACCACCATCAATC CTCCAGTTCCCACTGTCCTC NAGLU W07099TCCAACAGCACCAGTTTGAC AGCCGGGGTAATATTTGAGG NAGLU W07099ACACTCCGGAGCAGTAGCC AAAGGACCCAGTGCCAGATT ROBO1 AA173755CTGACCCCAGTGGAAAACA CCCTTAGTACTGCACGCCTTT 5-HTT BC069484CCTCCAGCCACTTATTTCCA ACCTCCATCCACATCCTCAC Control Primers MLC1 AA196486TGAAGAGCTGAATGCCAAGA CCTTGTCAAAGACACGCAGA TCRb AA909476CATGAGCATCAGCCTTCTGT GAAAGGCCTGTCCACTCTCC Werner HIP AA189052GGCTATGGCAAAGGCTACAA GAGTCAGCACCTCCTCTGCT

Forward primer sequence identifiers from top to bottom: SEQ ID NOS: 1,3, 5, 7, 9 11, 13, 15, 17, 19, 21, 23, 25, 27, 29.

Reverse primer sequences identifiers from top to bottom are: SEQ ID NOS:2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30.

What is claimed is:
 1. A composition comprising: an ensemble consistingof nucleic acid molecules produced from a sample of peripheral bloodcells of an individual having autism hybridized to a nucleic acidmolecule probe spatially arrayed and affixed to a solid surface for eachof argininosuccinate synthetase (ASS), interleukin-6 signal transducer(IL6ST), death-associated protein kinase 1 (DAPK1), 5-lipoxygenaseactivating protein (FLAP (ALOX5AP)), and Roundabout homolog 1 precursor(ROBO1), wherein each of the nucleic acid molecules produced from thesample contain a label that is capable of emitting a detectable signal.2. A composition comprising an ensemble consisting of nucleic acidmolecules produced from a sample of peripheral blood cells of anindividual having autism hybridized to nucleic acid molecule probespatially arrayed and affixed to a solid surface for each ofargininosuccinate synthetase (ASS) interleukin-6 signal transducer(IL6ST), death-associated protein kinase (DAPK1), 5-lipoxygenaseactivating protein (FLAP (ALOX5AP), Roundabout homolog 1 precursor(ROBO1), and at least one of integrin beta-7LCL: lymphoblastoid celllines (ITGB7), cell adhesion molecule with homology to L1CAM (CHL1), and5-hydroxytryptamine (serotonin) transporter (5-HTT)), wherein each ofthe nucleic acid molecules produced from the sample contain a label thatis capable of emitting a detectable signal.
 3. The composition of claim1, wherein the sample of peripheral blood cells comprises white bloodcells or their lymphoblast derivatives.
 4. The composition of claim 2,wherein the sample of peripheral blood cells comprises white blood cellsor their lymphoblast derivatives.
 5. The composition of claim 1, whereineach of the probes contains from about 15 to about 25 nucleotides. 6.The composition of claim 1, wherein the label is fluorescent.
 7. Anarray consisting of nucleic acid probes for each of argininosuccinatesynthetase (ASS), interleukin-6 signal transducer (IL6ST),death-associated protein kinase (DAPK1), 5-lipoxygenase activatingprotein (FLAP (ALOX5AP)), and Roundabout homolog precursor (ROBO1),wherein the probes are spatially arrayed and affixed to a solid surface.8. The array of claim 7, wherein each of the probes are from about 15 toabout 25 nucleotides in size.
 9. The array of claim 7, wherein at leastone of the probes is bound to a nucleic acid molecule to form a complex,wherein the complex emits a detectable signal.
 10. The array of claim 9,wherein the detectable signal is fluorescent.